Feedforward Neural Network Architectures for Complex Classi cation Problems
نویسندگان
چکیده
This paper presents two neural network design strategies for incorporating a pri-ori knowledge about a given problem into the feedforward neural networks. These strategies aim at obtaining tractability and reliability for solving complex classiica-tion problems by neural networks. The rst type strategy based on multistage scheme decomposes the problem into manageable ones for reducing the complexity of the problem , and the second type strategy on multiple network scheme combines incomplete decisions from several copies of networks for reliable decision-making. A preliminary experiment of recognizing on-line handwriting characters connrms the superiority relative to a single large neural network classiier.
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